A website publisher strives to present a website that serves its users (e.g., visitors to the website) with a good website experience. One way the website publisher attempts to provide a good website experience to users is to present targeted content to its users. The content may be advertisements, news content, social media content, or online offers for products or experiences offered through an online marketplace. To effectively generate targeted content for its users, website publishers predict the targeted content based on observable user information gathered through the users' interaction on the Internet. The observable user information may include user attributes included in an online user profile of the user, or online website browser history data attributed to the user.
However, with the popularity of online website browsing, the amount of observable user information related to a user's interactions on the Internet has grown immensely. Filtering through the immense amount of observable user information is a difficult task that requires large amounts of computer processing capabilities and time to sort through. The time attributable to analyzing the observable user information to generate a prediction on enjoyable content to a user also takes away from the user's website experience.